Convolutional Networks 2019: We have released our stereo blur dataset [. From Fig. The original CBCT images are shown in the 1st column, and the segmentation results in 2D and 3D views are shown in the 2nd and 3rd columns, respectively. 2137, Springer, Amsterdam, The Netherlands, October 2016. Convolutional networks are powerful visual models that yield hierarchies of features. Jifeng Dai The framework was implemented in PyTorch library45, using the Adam optimizer to minimize the loss functions and to optimize network parameters by back propagation. fully Mar. The pretrained SSD_mobilenet_v1_COCO model with the COCO dataset is used to learn the characteristics of the safety helmet in the built dataset to reduce the training time and save the computing resources. At the construction site, workers that wear safety helmets improperly are much more likely to be injured. Specifically, as shown in Fig. Video monitoring systems provide a large amount of unstructured image data on-site for this purpose, however, requiring a computer vision-based automatic solution for real-time Dec. 2021: Two papers are accepted by AAAI 2022. Note that these two expert radiologists are not the people for ground-truth label annotation. Clipboard, Search History, and several other advanced features are temporarily unavailable. 44, pp. Sohaib Shujaat, Marryam Riaz & Reinhilde Jacobs, Sorana Mureanu, Oana Alman, Reinhilde Jacobs, Nature Communications Specifically, Dice is used to measure the spatial overlap between the segmentation result \(R\) and the ground-truth result G, defined as Dice=\(\frac{2\left|R\cap G\right|}{\left|R\right|+\left|G\right|}\). J. The authors declare that they have no conflicts of interest regarding the publication of this paper. [Paper] Code is available! Article As shown in Table3, by applying the data argumentation techniques (e.g., image flip, rotation, random deformation, and conditional generative model38), the segmentation accuracy of different competing methods indeed can be boosted. Fully convolutional networks for semantic segmentation. volume13, Articlenumber:2096 (2022) Semantic H. Luo, C. Xiong, W. Fang, P. E. D. Love, B. Zhang, and X. Ouyang, Convolutional neural networks: computer vision-based workforce activity assessment in construction, Automation in Construction, vol. Our AI system can more robustly handle the challenging cases than CGDNet, as demonstrated by the comparisons in Supplementary Table3, using either small-size dataset or large-scale dataset. 1c, where the individual teeth and surrounding bones are marked with different colors. ADS In Figure 10(b), the red helmet is missed and this is a case of false negative. However, most of the studies have limitations in practical application. project page, Unsupervised Learning of Dictionaries of Hierarchical Compositional Models 46, 106117 (2018). Deformable DETR: Deformable Transformers for End-to-End Object Detection In contrast, since the external dataset is collected from different dental clinics, the distribution of its dental abnormalities is a little different compared with the internal set. X. Chang and X. M. Liu, Fault tree analysis of unreasonably wearing helmets for builders, Journal of Jilin Jianzhu University, vol. First, we highlight convolution with upsampled filters, or `atrous convolution', as a powerful tool in dense prediction tasks. Automatic medical image segmentation plays a critical role in scientific research and medical care. [Supplemental material] The contributions of the research include a deep learning-based safety helmet detection model and a safety helmet image dataset for further research. It is possible to recognize the objects of the same colors in the images as the safety helmets. Nevertheless, many traditional measures of safety helmet detection are commonly sensor-based and machine-based, thus limited by problems such as sensor failure over long distances, the manual and subjective features choice, and the chaotic scene interference. In clinical practice of dental treatments, medical imaging with different modalities, such as 2D panoramic X-rays, 3D intra-oral scans, and 3D cone-beam computed tomography (CBCT) images, are commonly acquired to assist diagnosis, treatment planning, and surgery. Fang et al. Relation Networks for Object Detection [Paper] The output of the network is a 3-channel mask, with the same size as the input patch, indicating probabilities of each voxel belonging to the background, midface bone, and mandible bone, respectively. International Conference on Computer Vision (ICCV), 2015. The sizes of the convolutional feature maps decrease progressively to predict the detections at multiple scales. S. Barro-Torres, T. M. Fernndez-Carams, H. J. Prez-Iglesias, and C. J. Escudero, Real-time personal protective equipment monitoring system, Computer Communications, vol. Med. Electron. CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Networks (SIANN), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide Article Head injuries are very serious and often fatal. The authors declare that partial data (i.e., 50 raw data of CBCT scans collected from dental clinics) will be released to support the results in this study (link: https://pan.baidu.com/s/1LdyUA2QZvmU6ncXKl_bDTw, password:1234), with permission from respective data centers. Neurocomputing 419, 108125 (2021). and transmitted securely. Visual examination of the workplace and in-time reminder to the failure of wearing a safety helmet is of particular importance to avoid injuries of workers at the construction site. Automatic medical image segmentation plays a critical role in scientific research and medical care. In Proceedings of the IEEE International Conference on Computer Vision, 29612969 (2017). Code is available! Science 349, 261266 (2015). Before that, I got my B.S. Gan, Y. et al. The methods construct convolutional neural networks with different depths to detect safety helmets. K. Shrestha, P. P. Shrestha, D. Bajracharya, and E. A. Yfantis, Hard-hat detection for construction safety visualization, Journal of Construction Engineering, vol. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. The convergence of the loss functions demonstrates that the training of the model is completed. More importantly, since all the CBCT images are scanned from patients with dental problems, different centers may have large different distributions in dental abnormalities, which further increases variations in tooth/bone structures (i.e., shape or size). Github: https://github.com/fundamentalvision https://github.com/msracver https://github.com/daijifeng001. (Invited paper). The main function is to reduce the calculation amount and the network parameters. & Culurciello, E. LinkNet: exploiting encoder representations for efficient semantic segmentation. Arxiv Tech Report, 2022. 7 ). International Conference on Vis. Z.C., Y.F., and L.M. The test set is used to evaluate the generalization ability of the final model [28]. The errors occur because of the interference of the complex background, the limitation of the number of the image dataset, and the safety helmets proportion in the images. 2018: Six papers (including one Spotlight) are accepted by CVPR 2018. IEEE Trans. Automatic segmentation of individual tooth in dental CBCT images from tooth surface map by a multi-task fcn. Visual examination of the workplace and in-time reminder to the failure of wearing a safety helmet is of particular importance to avoid injuries of workers at the construction site. ECNet: Efficient Convolutional Networks for Side Scan Sonar Image Segmentation. Arxiv Tech Report, 2018. Hao Li*+, Chenxin Tao*+, Xizhou Zhu, Xiaogang Wang, Gao Huang, and Jifeng Dai Individual tooth segmentation from CT images using level set method with shape and intensity prior. 3D SOD: Add one IJCV paper, one IEEE TNNLS paper, two IEEE TIP papers, two ECCV22 papers. Jinshan Pan's Homepage - GitHub Pages 69, 987997 (2005). Deep Feature Flow for Video Recognition (Oral), Ridge Based Palmprint Matching (a) Image with GT box. Image Anal. 2, 158164 (2018). This further demonstrates the importance of collecting large-scale dataset in clinical practice. Huang and Professor W.D. [Paper] Deep embedding convolutional neural network for synthesizing ct image from t1-weighted mr image. [24] proposed YOLO (You Only Look Once) algorithm in 2016. Would you like email updates of new search results? Center-sensitive and boundary-aware tooth instance segmentation and classification from cone-beam CT. Lett. 88, no. However, the detection model has a poor performance when the images are not very clear, the safety helmets are too small and obscure, and the background is too complex as shown in Figure 10. Also, the worker close to the camera failed to be recognized. In this sense, an adequate ratio of 8:1:1 according to the previous experience is adopted in our study. segmentation The proposed GFSAE module is placed between the down-sampling and up-sampling networks for semantic segmentation of large-scale urban street-level point clouds. Sun, Spatial pyramid pooling in deep convolutional networks for visual recognition, IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. [Code (PyTorch implementation)], Jiawei Zhang, Jinshan Pan, Jimmy Ren, Yibing Song, Linchao Bao, Rynson Lau, and Ming-Hsuan Yang, "Dynamic Scene Deblurring Using Spatially Variant Recurrent Neural Networks", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. In addition, it consistently obtains accurate results on the challenging cases with variable dental abnormalities, with the average Dice scores of 91.5% and 93.0% for tooth and alveolar bone segmentation. Jul. The authors declare no competing interests. GitHub This is because teeth are relatively small objects, and neighboring teeth usually have blurry boundaries, especially at the interface between upper and lower teeth under a normal bite condition. The early network layers of the SSD model are called the base network, based on a standard framework to classify the image. Jinshan Pan's Homepage - GitHub Pages [Paper] We present a conceptually simple, flexible, and general framework for object instance segmentation. Additional refinements can make the dental diagnosis or treatments more reliable. In order to improve the performance of the model, some measures must be taken such as increasing the number of the image dataset and adding the preprocessing operations of the images. Remarkable studies include the following: Ding et al. The model also introduces two hyperparameters: width multiplier and resolution multiplier to reduce the channel numbers and reduce the image resolutions, respectively. Peer reviewer reports are available. It can be seen that the 3D dental models reconstructed by our AI system have much smoother surfaces compared to those annotated manually by expert radiologists. Convolutional Networks Real-time semantic segmentation of aerial imagery is essential for Unmanned Ariel Vehicle (UAV) applications, including military surveillance, land characterization, and disaster damage assessments. b The morphology-guided network is designed to segment individual teeth. Pattern Recognit. Note that, in the inference time, a post-processing step is employed to merge the predicted bone and tooth masks. In each labeled image, the sizes and the locations of the object are recorded (Figure 5). In the PNN algorithm, the parent probability distribution function (PDF) of each class is approximated by a Parzen window and a non-parametric function. [Project] [paper] Recall is the ratio of true positive (TP) to true positive and false negative (TP+FN). Hiew, L., Ong, S., Foong, K. W. & Weng, C. Tooth segmentation from cone-beam ct using graph cut. Therefore, the overall work time includes the time verifying and updating segmentation results from our AI system. Biol. Dent. Nature 542, 115118 (2017). Chenxin Tao*+, Zizhang Li*+, Xizhou Zhu+, Gao Huang, Yong Liu, Jifeng Dai [Project] Fully Convolutional Networks for Semantic Segmentation Arxiv Tech Report, 2022. 1). [Code], Jinshan Pan, Wenqi Ren, Zhe Hu, and Ming-Hsuan Yang, "Learning to Deblur Images with Exemplars", IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2018. Toward accurate tooth segmentation from computed tomography images using a hybrid level set model. 2017 Mar 24;12(3):e0174508. Generative adversarial network International Conference on Computer Vision (ICCV), 2017. B.Z., B.Y., Y.L., Y.Z., Z.D., and M.Z. Traditional supervision of the workers wearing safety helmets on construction sites often requires manual work [2]. Accordingly, we also compute corresponding p values to validate whether the improvements are statistically significant. I was a Principle Research Manager in Visual Computing Group at Microsoft Research Asia (MSRA) between 2014 and 2019, headed by Dr. Jian Sun. The precision of the trained model is 95% and the recall is 77%, which demonstrates that the proposed method performs well in safety helmet detection. In this sense, we first apply an encoder-decoder network to automatically segment the foreground tooth for dental area localization. Pose-aware instance segmentation framework from cone beam CT images for tooth segmentation. & Berkey, D. B. Comput. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. Code is available! However, these methods have some limitations in the preprocessing aspects of image sharpness, object proportion, and the color difference between background and foreground. 3 and Table2 have also shown that our AI system can produce consistent and accurate segmentation on both internal and external datasets with various challenging cases collected from multiple unseen dental clinics. Fully Convolutional Networks for Semantic Segmentation c Qualitative comparison of tooth and bone segmentation on the four center sets. [Project] [MATLAB code] Segmentation Another important contribution of this study is that we have conducted a series of experiments and clinical applicability tests on a large-scale dataset collected from multi-center clinics, demonstrating that deep learning has great potential in digital dental dentistry. Eng. Our AI system can increase Dice score by 2.7% on internal testing set, and 2.6% on external testing set, respectively. 313, no. This is mainly due to the two proposed complementary strategies for explicitly enhancing the network learning of tooth geometric shapes in the CBCT images (especially with metal artifacts or blurry boundaries). Often, the convolutional layer and the pooling layer may occur alternately. [Test code], Jinshan Pan, Zhe Hu, Zhixun Su, and Ming-Hsuan Yang, "Deblurring Text Images via L0-Regularized Intensity and Gradient Prior", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014. 118124, 2018. Front Reprod Health. [Paper] () Then, using PDF of each class, the class probability of a new input is Convolutional Networks Moreover, we also introduce a filter-enhanced (i.e., Harr transform) cascaded network for accurate bone segmentation by enhancing intensity contrasts between alveolar bones and soft tissues. The study has some restrictions: it focuses on a limited number of activities related to the construction of deep foundation-pits. The automatic monitoring method can contribute to monitoring the construction workers and confirm the safety helmet wearing conditions at the construction site. In 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 11971200 (IEEE, 2017). The experiment results demonstrate that the method can be used to detect the safety helmets worn by the construction workers at the construction site. Multi-feature Based High-resolution Palmprint Recognition S. Xu, Y. Wang, Y. Gu, N. Li, L. Zhuang, and L. Shi, Safety helmet wearing detection study based on improved faster RCNN, Application Research of Computers, vol. (Oral) Furthermore, extensive clinical validations and comparisons with expert radiologists have verified the clinical applicability of our AI system, especially in greatly reducing human efforts in manual annotation and inspection of the 3D tooth and alveolar bone segmentations. 3431-3440). 60, 101621 (2020). Then, they check the initial results slice-by-slice and perform manual corrections when necessary, i.e., when the outputs from our AI system are problematic according to their clinical experience. Rev. 2. R. Girshick, J. Donahue, T. Darrell, and J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmentation. The validation set is used to adjust the hyperparameters of the model and to evaluate the capacity of the model preliminarily. 37, no. [Project] The width of the default boxes is calculated as follows: The height of the default boxes is calculated as follows: When the aspect ratio is 1, a default box size is added: . International Conference on Computer Vision (ICCV), 2013. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. First, fully automatic tooth and alveolar bone segmentation is complex consisting of at least three main steps, including dental region of interest (ROI) localization, tooth segmentation, and alveolar bone segmentation. 179177, 2019. (Oral) Context-guided fully convolutional networks for joint craniomaxillofacial bone segmentation and landmark digitization. Our key insight is to build fully convolutional networks that take input of arbitrary size and produce correspondingly-sized Deformable Kernels: Adapting Effective Receptive Fields for Object Deformation J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, You only look once: unified, real-time object detection, in Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, June 2016. However, the quality of the crawled images varies greatly. Atrous convolution allows us to explicitly control the resolution 36, pp. Also, due to the above challenge, the segmentation efficiency of expert radiologists is significantly worse than our AI system. Imagenet: a large-scale hierarchical image database. Although the SSD algorithm performs well in the speed and the precision, the large model and a large amount of calculation make the training speed a bit slow. [Paper] Therefore, considering the real-time detection requirements, the SSD algorithm is chosen in the research. In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 939942 (IEEE, 2020). Convolution for Semantic Image Learning-Based Safety Helmet Detection [MATLAB code], Jinshan Pan, Jiangxin Dong, Yu-Wing Tai, Zhixun Su, and Ming-Hsuan Yang, "Learning Discriminative Data Fitting Functions for Blind Image Deblurring", IEEE International Conference on Computer Vision (ICCV), 2017. School of Biomedical Engineering, ShanghaiTech University, Shanghai, 201210, China, Zhiming Cui,Yu Fang,Lanzhuju Mei,Jiameng Liu,Caiwen Jiang,Yuhang Sun,Lei Ma,Jiawei Huang&Dinggang Shen, Department of Computer Science, The University of Hong Kong, Hong Kong, 999077, China, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, 200030, China, Shanghai Ninth Peoples Hospital, Shanghai Jiao Tong University, Shanghai, 200011, China, School of Public Health, Hangzhou Medical College, Hangzhou, 310013, China, Department of Orthodontics, Stomatological Hospital of Chongqing Medical University, Chongqing, 401147, China, School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China, School of Mathematics and Statistics, Xian Jiaotong University, Xian, 710049, China, Department of Radiology, Hangzhou First Peoples Hospital, Zhejiang University, Hangzhou, 310006, China, You can also search for this author in International Conference on Computer Vision (ICCV), 2019. _-CSDN_ 4e, f, we can see that our AI system still achieves promising results, even for the extreme case with an impacted tooth as highlighted by the red box in Fig.